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Successfully recommending relevant content to web users is critical to business success for may web properties. There are a variety of techniques used, with the largest sites and companies employing some highly sophisticated technology to optimize their recommendations. In this blog post we discuss how to build an effective recommendation system from first principles in ruby.
Read article about These days companies are finding new ways to personalise fashion for their shoppers, leveraging technology application across channels. However, as the factories that can adapt to personalised production are few, they need to rely on high volume orders to survive. This article explores what personalisation means for the future of global fashion. and more articles about Textile industary at Fibre2Fashion
Learn how to design systems at scale and prepare for system design interviews - GitHub - karanpratapsingh/system-design: Learn how to design systems at scale and prepare for system design interviews
The global recommendation engine market is driven by factors such as rise in adoption of digital technologies, increase in focus to enhance customer experience, and increase in use of the deep
Recommender systems help Internet users quickly find information they may be interested in from an extremely large amount of resources. Recent studies have shown that incorporating auxiliary social trust relationship information into the recommender system improves the accuracy of recommendations. Most existing research only considers explicit trust relationships, which result in sub-optimal recommendation performance. In this research, we present a trust model which analyses user trustworthiness based on user’s behaviours on the social networks. The proposed trust model increases the densit...